Keywords

Abstract

The target intercept problem has two main components. The first is the development of a control loop for the interceptor. The second is the target-tracking system that provides the location of the target to the control law. The tracking system is a significant element,
particularly when predictive control is used and the target motion is unknown a priori. A neural Kalman filter approach to target tracking is presented as a technique to improve the motion model of the target while it is being tracked in flight. A linearized version
of that model is then used to provide an improved estimate of the predicted location of the target. The technique uses an augmented Kalman filter that couples the tracking capabilities and a neural network training algorithm. The motion model then becomes a composite of the a priori motion model and neural network. The model is then linearized at the state for which it was computed;
and this linearized model is used to propagate the state estimate forward to a given intercept time. The improved model generally gives a better result than the standard straight-line motion tracking performance, even with target maneuvering included in the process noise.